Multi-model selection for neural network based tools in video coding

ABSTRACT

A method, computer program, and computer system is provided for video encoding and decoding. Video data including one or more frames is received. One or more quantization parameters associated with the received video data are determined for frame generation or enhancement of a target frame from among the one or more frames. The video data is decoded based on the determined quantization parameters.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from U.S. Provisional PatentApplication No. 63/136,062 (filed Jan. 11, 2021) in the U.S. Patent andTrademark Office, the entirety of which is herein incorporated byreference.

FIELD

This disclosure relates generally to field of data processing, and moreparticularly to video coding.

BACKGROUND

Video coding and decoding using inter-picture prediction with motioncompensation has been known for decades. Uncompressed digital video canconsist of a series of pictures, each picture having a spatial dimensionof, for example, 1920×1080 luminance samples and associated chrominancesamples. The series of pictures can have a fixed or variable picturerate (informally also known as frame rate), of, for example 60 picturesper second or 60 Hz. Uncompressed video has significant bitraterequirements. For example, 1080p60 4:2:0 video at 8 bit per sample(1920×1080 luminance sample resolution at 60 Hz frame rate) requiresclose to 1.5 Gbit/s bandwidth. An hour of such video requires more than600 GByte of storage space.

Traditional video coding standards, such as the H.264/Advanced VideoCoding (H.264/AVC), High-Efficiency Video Coding (HEVC) and VersatileVideo Coding (VVC) share a similar (recursive) block-based hybridprediction/transform framework where individual coding tools like theintra/inter prediction, integer transforms, and context-adaptive entropycoding, are intensively handcrafted to optimize the overall efficiency.

SUMMARY

Embodiments relate to a method, system, and computer readable medium forvideo coding. According to one aspect, a method for video coding isprovided. The method may include receiving video data include one ormore frames. One or more quantization parameters associated with thereceived video data are determined for frame generation or enhancementof a target frame from among the one or more frames. The video data isdecoded based on the determined quantization parameters.

According to another aspect, a computer system for video coding isprovided. The computer system may include one or more processors, one ormore computer-readable memories, one or more computer-readable tangiblestorage devices, and program instructions stored on at least one of theone or more storage devices for execution by at least one of the one ormore processors via at least one of the one or more memories, wherebythe computer system is capable of performing a method. The method mayinclude receiving video data including one or more frames. One or morequantization parameters associated with the received video data aredetermined for frame generation or enhancement of a target frame fromamong the one or more frames. The video data is decoded based on thedetermined quantization parameters.

According to yet another aspect, a computer readable medium for videocoding is provided. The computer readable medium may include one or morecomputer-readable storage devices and program instructions stored on atleast one of the one or more tangible storage devices, the programinstructions executable by a processor. The program instructions areexecutable by a processor for performing a method that may accordinglyinclude receiving video data including one or more frames. One or morequantization parameters associated with the received video data aredetermined for frame generation or enhancement of a target frame fromamong the one or more frames. The video data is decoded based on thedetermined quantization parameters.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is a hierarchical temporal structure for loopfilter/inter-prediction, according to at least one embodiment;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program that encodes and decodes video data, according to at least oneembodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, according to at leastone embodiment; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and moreparticularly to video coding. The following described exemplaryembodiments provide a system, method and computer program to, amongother things, encode and decode video data based on a hierarchicaltemporal structure for loop filter/inter-prediction. Therefore, someembodiments have the capacity to improve the field of computing byallowing for improved efficiency in video coding.

As previously described, Video coding and decoding using inter-pictureprediction with motion compensation has been known for decades.Uncompressed digital video can consist of a series of pictures, eachpicture having a spatial dimension of, for example, 1920×1080 luminancesamples and associated chrominance samples. The series of pictures canhave a fixed or variable picture rate (informally also known as framerate), of, for example 60 pictures per second or 60 Hz. Uncompressedvideo has significant bitrate requirements. For example, 1080p60 4:2:0video at 8 bit per sample (1920×1080 luminance sample resolution at 60Hz frame rate) requires close to 1.5 Gbit/s bandwidth. An hour of suchvideo requires more than 600 GByte of storage space. Traditional videocoding standards, such as the H.264/Advanced Video Coding (H.264/AVC),High-Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC)share a similar (recursive) block-based hybrid prediction/transformframework where individual coding tools like the intra/inter prediction,integer transforms, and context-adaptive entropy coding, are intensivelyhandcrafted to optimize the overall efficiency.

One purpose of video coding and decoding can be the reduction ofredundancy in the input video signal, through compression. Compressioncan help reducing aforementioned bandwidth or storage spacerequirements, in some cases by two orders of magnitude or more. Bothlossless and lossy compression, as well as a combination thereof can beemployed. Lossless compression refers to techniques where an exact copyof the original signal can be reconstructed from the compressed originalsignal. When using lossy compression, the reconstructed signal may notbe identical to the original signal, but the distortion between originaland reconstructed signal is small enough to make the reconstructedsignal useful for the intended application. In the case of video, lossycompression is widely employed. The amount of distortion tolerateddepends on the application; for example, users of certain consumerstreaming applications may tolerate higher distortion than users oftelevision contribution applications. The compression ratio achievablecan reflect that: higher allowable/tolerable distortion can yield highercompression ratios.

The spatiotemporal pixel neighborhoods are leveraged for predictivesignal construction, to obtain corresponding residuals for subsequenttransform, quantization, and entropy coding. On the other hand, thenature of Neural Networks (NN) is to extract different levels ofspatiotemporal stimuli by analyzing spatiotemporal information from thereceptive field of neighboring pixels. The capability of exploringhighly nonlinearity and nonlocal spatiotemporal correlations providepromising opportunity for largely improved compression quality.

However, one caveat of leveraging information from multiple neighboringvideo frames is the complex motion caused by moving camera and dynamicscenes. Traditional block-based motion vectors cannot work well fornon-translational motions. Learning based optical flow methods canprovide accurate motion information at pixel-level, which is,unfortunately prone to error, especially along the boundary of movingobjects. In some Hybrid Inter-frame prediction, NN-based model isapplied to implicitly handle arbitrary complex motion in a data-drivenfashion.

It may be advantageous, therefore, to select different frames asreference frame to apply LF or generate intermediate frame when usingNN-based model as Loop Filter (LF) or for Inter-prediction tools inorder to have a better trade-off for performance and coding runtime.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

The following described exemplary embodiments provide a system, methodand computer program that encodes and decodes video data. Referring nowto FIG. 1, a functional block diagram of a networked computerenvironment illustrating a video coding system 100 (hereinafter“system”) for encoding and decoding video data. It should be appreciatedthat FIG. 1 provides only an illustration of one implementation and doesnot imply any limitations with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 4 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below withrespect to FIGS. 5 and 6. The server computer 114 may also be located ina cloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for encoding and decodingvideo data is enabled to run a Video Coding Program 116 (hereinafter“program”) that may interact with a database 112. The Video CodingProgram method is explained in more detail below with respect to FIG. 3.In one embodiment, the computer 102 may operate as an input deviceincluding a user interface while the program 116 may run primarily onserver computer 114. In an alternative embodiment, the program 116 mayrun primarily on one or more computers 102 while the server computer 114may be used for processing and storage of data used by the program 116.It should be noted that the program 116 may be a standalone program ormay be integrated into a larger video coding program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1. Furthermore, two or more devices shown in FIG. 1 may beimplemented within a single device, or a single device shown in FIG. 1may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2, a hierarchical temporal structure 200 for loopfilter/inter-prediction is depicted. The hierarchical structure 200 mayapply NN-based Loop Filter or Inter-Frame Prediction in video coding anddecoding, and more specifically, to determine the number/index of frameto be applied as reference frame to use NN model for frame generation orenhancement in video coding for I/P/B frame respectively. Assume aninput video x comprising of a plurality of image frames x₁, . . . ,x_(T) (e.g., 1-16). In a first motion estimation step, the frames arepartitioned into spatial blocks, each block can be partitioned intosmaller blocks iteratively, and a set of motion vectors mt between acurrent frame x_(t) and a set of previous reconstructed frames{{circumflex over (x)}_(j)}_(t-31 1) is computed for each block. Notethat the subscript t denotes the current t-th encoding cycle, which maynot match the display order (time stamp) of the image frame. Also,{{circumflex over (x)}_(j)}_(t-1) contains frames from multiple previousencoding cycles. Then, in a second motion compensation step, for acurrent coding block, in the current frame {tilde over (x)}_(t), apredicted block is obtained by copying the corresponding pixels of theprevious {{circumflex over (x)}_(j)}_(t-1) based on the motion vectorsm_(t), and a residual r_(t) between the original block and the predictedblock can be obtained. In the third step, the residual r_(t) istransformed then quantized.

A quantization step gives a quantized transform block. Both the motionvectors mt and the quantized transform block are encoded into bit steamsby entropy coding, which are sent to decoders. Then on the decoder side,the decoded block will apply inverse transform and dequantization(typically through inverse transformation like IDCT with the dequantizedcoefficients) to obtain a recovered residual {circumflex over (r)}_(t).Then {circumflex over (r)}_(t) is added back to the predictor block toobtain reconstructed block. Additional components are further used toimprove the visual quality of the reconstructed {circumflex over(x)}_(t). Typically, one or multiple of the following enhancementmodules can be selected to process {circumflex over (x)}_(t), includingDeblocking Filter (DF), Sample-Adaptive Offset (SAO), Adaptive LoopFilter (ALF), etc.

In HEVC, VVC or other video coding frameworks or standards, the decodedpictures may be included in the reference picture list (RPL) and may beused for motion-compensated prediction as a reference picture and otherparameter prediction for coding the following picture(s) in the encodingor decoding order. Or the decoded part of a current picture may be usedfor intra-prediction or intra block copy for coding different region orblock of the current picture.

In an example, one or more virtual references may be generated andincluded in the RPL in both encoder and decoder, or only in decoder. Thevirtual reference picture may be generated by one or more processesincluding signal-processing, spatial or temporal filtering, scaling,weighted averaging, up-/down-sampling, pooling, recursive processingwith memory, linear system processing, non-linear system processing,neural-network processing, deep-learning based processing,AI-processing, pre-trained network processing, machine-learning basedprocessing, on-line training network processing or their combinations.For the processing to generate the virtual reference(s), zero or moreforward reference pictures, which precede the current picture in bothoutput/display order and en-/decoding order, and zero or more backwardreference pictures, which follow the current picture both inoutput/display order but precede the current picture in en-/decodingorder are used as input data. The output of the processing is thevirtual/generated picture to be used as a new reference picture. Regularmotion compensation technologies may be applied when this new referencepicture is chosen to predict a coding block in the current picture.

In an example, NN based methods may be applied to in-loop filter designat both Slice/CTU level on each frame, in combination with one ormultiple of the above-mentioned additional components (e.g., DF, SAO,ALF, CCALF etc.), or to replace one or multiple of the above-mentionedadditional components (e.g., DF, SAO, ALF, CCALF etc.). If applied, thereconstructed current picture will be used as input data to the NN basedmodel(s) to generate the NN enhanced filtered picture. For each block orCTU, the decision can be made whether to choose this NN enhancedfiltered picture as post filtering result or use traditional filteringmethods.

One or more NN models can be selected as NN based video coding tools fordifferent scenarios to apply to pictures that meet certain condition. Inone embodiment, for different QPs (quantization parameter), one or moreNN based models may be selected in the NN based video coding tools. Inanother word, NN-based video coding tools can use one model for all QPs,or separate models for each QP, or separate models with one modelassigned for a set of QP range.

In one or more embodiments, for pictures under different hierarchicallevels, one or more NN based models will be used in NN based videocoding tools. In another word, NN based video coding tools can use onemodel for all frames, or separate models for different frames that havetheir POCs meet certain condition. For example, FIG. 1 illustrates anexample of the hierarchical structure for loop filter/inter-prediction.One model is designed for frames having hierarchical levels ID equal to1, another model is designed for frames having hierarchical levels IDequal to 2, etc. In another example, for pictures that differenthierarchical levels ID that is even or odd may be applied multiplemodels in NN based video coding tools. In another word, one model isdesigned for frames have hierarchical levels ID is even, another modelis designed for frames have hierarchical levels ID is odd.

In one or more embodiments, for different frames that have differenttype of reference picture list (RPL), one or more NN based models may beselected in NN based video coding tools. In another word, NN based videocoding tools can use one model for all frames, or separate models fordifferent frames that its reference picture list (RPL) meet certaincondition. For example, one model is designed for frames that theirlength of reference picture list (RPL) is equal to 1, another model isdesigned for frames that their length of reference picture list (RPL) isequal to 2, etc. In another example, when the reference pictures in thereference picture list (RPL) comes from different hierarchical levels,multiple models may be applied in NN based video coding tools. Inanother word, one model is designed for frames that their referencepictures in the reference picture list (RPL) comes from hierarchicallevels 1 and 2, another model is designed for frames that theirreference pictures in reference picture list (RPL) comes fromhierarchical levels 1, 2 and 3, etc.

In one or more embodiments, for different frames that have differentnumber of reference frames as input for the NN based model, one or moreNN based model will be selected in the NN based video coding tools. Inanother word, NN based video coding tools can use one model for allframes, or separate models for different frames that use certain numberof reference frames as input for NN based model. For example, one modelis designed for frames that select one unique reference frame in the RPLas input to NN model; another model is designed for frames that selecttwo unique reference frames in the RPL as input to NN model, etc. Inanother example, the reference frames may not need to be presented inthe RPL, they could be, however, stored in the DPB so both encoder anddecoder can access them.

In one or more embodiments, for different frames that have differenttemporal distances to their reference frames as input for NN basedmodel, one or more NN based model will be selected in the NN based videocoding tools. In another word, NN based video coding tools can use onemodel for all frames, or separate models for different frames that theirreference frames may have different temporal distances to them. Forexample, one model is designed for current frames that use the referenceframes with temporal distance equal to 1 (to current frame) as input toNN model; another model is designed for current frames that have thereference frames with temporal distance equal to 2 (to current frame) asinput to NN model, etc.

In one or more embodiments, the NN based video coding may be NN basedinter-prediction or loop filtering or both.

A multiple model selection may be applied as NN-based coding tools invideo coding. The proposed method decides whether to select one or moreNN based models as neural-network-based coding tools to pictures bydifferent conditions. The NN based coding tools may include, but notlimited to, NN based loop filtering, NN based virtual reference picturefor inter prediction. Below are a few examples to further elaborate theproposed methods.

In one example, Given the hierarchical GOP structure in FIG. 1, onemodel can be used for all levels of pictures, or for each level,separate models may be used for different hierarchical level ID offrames (different condition of POC). As an example, for the currentpicture (with POC=3), it has different hierarchical level with the otherpicture (with POC=2), along with different scenario or condition, thesetwo pictures can use one common model, or separate models in NN-basedcoding tools.

In another example, Given the hierarchical GOP structure in FIG. 1, forthe current picture (with POC=3), it has reference picture list (RPL) ofpictures (with POC=0, 2, 4, 8), and for the current picture (withPOC=10), it has reference picture list (RPL) of pictures (with POC=8,12, 16), these two frames have different reference picture list (RPL)that reference frames are from different hierarchical levels, and suchtwo pictures can use one common model, or separate models as NN-basedcoding tools.

In another example, Given the hierarchical GOP structure in FIG. 1, forthe current picture (with POC=3), it has reference picture list (RPL) ofpictures (with POC=0, 2, 4, 8), different number of reference frames canbe fed into NN based model. When different number of reference framesare used as input, it can use one common model, or separate models asNN-based coding tools.

In another example, Given the hierarchical GOP structure in FIG. 1, forthe current picture (with POC=3), it has reference picture list (RPL) ofpictures (with POC=0, 2, 4, 8), for each reference frame, it may havedifferent temporal distances to current picture, for different temporaldistance of reference frame relative to current picture, it can beapplied using one common model, or separate models in NN-based codingtools.

Referring now to FIG. 3, an operational flowchart illustrating the stepsof a method 300 carried out by a program that encodes and decodes videodata is depicted.

At 302, the method 300 may include receiving video data including one ormore frames.

At 304, the method 300 may include determining one or more quantizationparameters associated with the received video data for frame generationor enhancement of a target frame from among the one or more frames.

At 306, the method 300 may include decoding the video data based on thedetermined quantization parameters.

It may be appreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 4 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 5. Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1)and the Video Coding Program 116 (FIG. 1) on server computer 114(FIG. 1) are stored on one or more of the respective computer-readabletangible storage devices 830 for execution by one or more of therespective processors 820 via one or more of the respective RAMs 822(which typically include cache memory). In the embodiment illustrated inFIG. 4, each of the computer-readable tangible storage devices 830 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 830 is asemiconductor storage device such as ROM 824, EPROM, flash memory, anoptical disk, a magneto-optic disk, a solid state disk, a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, and/or another type of non-transitory computer-readabletangible storage device that can store a computer program and digitalinformation.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the Video Coding Program 116 (FIG. 1) can bestored on one or more of the respective portable computer-readabletangible storage devices 936, read via the respective R/W drive orinterface 832 and loaded into the respective hard drive 830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1) and theVideo Coding Program 116 (FIG. 1) on the server computer 114 (FIG. 1)can be downloaded to the computer 102 (FIG. 1) and server computer 114from an external computer via a network (for example, the Internet, alocal area network or other, wide area network) and respective networkadapters or interfaces 836. From the network adapters or interfaces 836,the software program 108 and the Video Coding Program 116 on the servercomputer 114 are loaded into the respective hard drive 830. The networkmay comprise copper wires, optical fibers, wireless transmission,routers, firewalls, switches, gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 5, illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 6, a set of functional abstraction layers 600 providedby cloud computing environment 500 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Video Coding 96. Video Coding 96 mayencode and decode video data based on using multiple neural networkmodels for prediction and loop filtering.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of video coding, executable by aprocessor, comprising: receiving video data including one or moreframes; determining one or more quantization parameters associated withthe received video data for frame generation or enhancement of a targetframe from among the one or more frames; and decoding the video databased on the determined quantization parameters.
 2. The method of claim1, wherein one or more neural network models are selected for the framegeneration or enhancement based on the determined quantizationparameters.
 3. The method of claim 1, wherein the target famecorresponds to one or more from among an I-frame, a P-frame, and aB-frame.
 4. The method of claim 1, wherein the decoded video data isincluded in a reference picture list.
 5. The method of claim 4, whereinbased on the reference picture list, subsequent frames from among theone or more frames are decoded based on motion-compensated prediction,intra prediction, or intra block copy.
 6. The method of claim 4, whereinone or more virtual reference frames are generated and included in thereference picture list.
 7. The method of claim 6, wherein the virtualreference frames are generated based on one or more from amongsignal-processing, spatial or temporal filtering, scaling, weightedaveraging, up-/down-sampling, pooling, recursive processing with memory,linear system processing, non-linear system processing, neural-networkprocessing, deep-learning based processing, AI-processing, pre-trainednetwork processing, machine-learning based processing, and on-linetraining network processing.
 8. A computer system for video coding, thecomputer system comprising: one or more computer-readable non-transitorystorage media configured to store computer program code; and one or morecomputer processors configured to access said computer program code andoperate as instructed by said computer program code, said computerprogram code including: receiving code configured to cause the one ormore computer processors to receive video data including one or moreframes; determining code configured to cause the one or more computerprocessors to determine one or more quantization parameters associatedwith the received video data for frame generation or enhancement of atarget frame from among the one or more frames; and decoding codeconfigured to cause the one or more computer processors to decode thevideo data based on the determined quantization parameters.
 9. Thecomputer system of claim 8, wherein one or more neural network modelsare selected for the frame generation or enhancement based on thedetermined quantization parameters.
 10. The computer system of claim 8,wherein the target fame corresponds to one or more from among anI-frame, a P-frame, and a B-frame.
 11. The computer system of claim 8,wherein the decoded video data is included in a reference picture list.12. The computer system of claim 11, wherein based on the referencepicture list, subsequent frames from among the one or more frames aredecoded based on motion-compensated prediction, intra prediction, orintra block copy.
 13. The computer system of claim 11, wherein one ormore virtual reference frames are generated and included in thereference picture list.
 14. The computer system of claim 13, wherein thevirtual reference frames are generated based on one or more from amongsignal-processing, spatial or temporal filtering, scaling, weightedaveraging, up-/down-sampling, pooling, recursive processing with memory,linear system processing, non-linear system processing, neural-networkprocessing, deep-learning based processing, AI-processing, pre-trainednetwork processing, machine-learning based processing, and on-linetraining network processing.
 15. A non-transitory computer readablemedium having stored thereon a computer program for video coding, thecomputer program configured to cause one or more computer processors to:receive video data including one or more frames; determine one or morequantization parameters associated with the received video data forframe generation or enhancement of a target frame from among the one ormore frames; and decode the video data based on the determinedquantization parameters.
 16. The computer readable medium of claim 15,wherein one or more neural network models are selected for the framegeneration or enhancement based on the determined quantizationparameters.
 17. The computer readable medium of claim 15, wherein thetarget fame corresponds to one or more from among an I-frame, a P-frame,and a B-frame.
 18. The computer readable medium of claim 15, wherein thedecoded video data is included in a reference picture list.
 19. Thecomputer readable medium of claim 18, wherein based on the referencepicture list, subsequent frames from among the one or more frames aredecoded based on motion-compensated prediction, intra prediction, orintra block copy.
 20. The computer readable medium of claim 18, whereinone or more virtual reference frames are generated and included in thereference picture list.